Waveform detector and state monitoring system using it

ABSTRACT

The present invention provides a waveform detection system featuring a signal-processing function that characterizes and detects non-cyclic transient variations and performs 1/f fluctuation conversion for input waveforms to derive output waveforms, and a state-monitoring system that uses the waveform detection system.  
     For this reason, the waveform detection system of the present invention that characterizes signs of state variations incorporates multiple digital filters in the digital filter calculator of the computer; uses coefficient patterns derived from non-integer n-time integration as elemental patterns for multiplication coefficient patterns; and incorporates a means to change the phase of at least one of the elemental patterns, input signal data, and digital filter output (phase-matching parameter setter) so that the outputs of digital filters that use the elemental patterns are synthesized in a state where a portion of the phases of the characteristic extracting and processing function is changed.

FIELD OF THE INVENTION

[0001] The present invention relates to a waveform detection system witha signal processing function that detects non-cyclic transient statevariations by characterizing and generating output waveforms by 1/ffluctuating input waveforms, and a state-monitoring system using thewaveform detection system.

BACKGROUND ART

[0002] A wavelet method, commercialized as a result of recentdevelopments of computer technology, is used to search for signs intime-series data in a state-monitoring system. The wavelet method isinferior to Fourier transform in terms of accuracy of spectrum analysisbut it is capable of dynamic analysis. The wavelet method moreaccurately captures spectral changes in both time-series data andimages. Currently, the wavelet method is extensively used to detectsigns in time-series data and recognize images in image data.

[0003]FIGS. 37 and 38 show the structure of a waveform detection systembased on the existing wavelet method (hereafter called the waveletsystem). The wavelet system comprises, as shown in the figures, a sensor3, signal input 1, computer 9, determinator 20, and output 25. Inputsignals are processed at the computer 9 to detect waveforms.

[0004] As shown in FIG. 37, the signal input comprises a converter 2that collects data from the sensor 3, A/D converter 4, memory 5, anddata fetcher 7. Measurement values from the sensor 3 are converted intodigital data by the A/D converter 4. Input signals are stored in filesin the memory S. Data are supplied as the processing at the computerprogresses. The necessary measurement input signal data 8 are created atthe computer 9.

[0005] The computer 9 comprises a signal processor 11, digital filtercalculator 13 comprising multiple digital filters Df1-Dfn, a parametersetter 15, and a synthesizer 17 that integrates the outputs from thedigital filters Df1-Dfn. The signal processor 11 at the computer 9 isnot essential. It is included as required. Individually, the signalprocessor processes noise in the input signals, normalizes data, anddistributes multiple data.

[0006] The structure of the digital filters Df1-Dfn in the computer isdescribed as shown in FIGS. 39 and 40. In FIG. 37, each digital filterDf comprises a delay memory 16 that stores and delays input signals, anda multiplication coefficient pattern memory 14 that storesmultiplication coefficients. In FIG. 38, the parameter setter 15 has afilter parameter setter 29 and a multiplication coefficient patternsetter 30. The computer 9 has a delay memory 16 on the digital filtersDf1-Dfn.

[0007] As shown in FIG. 40, the digital filter Df outputs the sums ofproducts of input signals and preset multiplication coefficientpatterns. The output is large when a waveform close to the shape of themultiplication coefficient pattern is present in the input signals. Asshown in FIG. 39, the data in the memory is carried forward as new dataenters the memory, so that the filter Df outputs the characteristics atthat time together with the time increment. The array of the outputs isthus a distribution of component intensities at a given time, showingthe signal characteristics. If the combination of characteristicscorrelates with the pattern of certain time-series data or the shape ofa symbol code, it is possible to detect signs showing variations in thestate of time-series data, identify and fetch symbols or codes of animage, or predict changes in input waveforms by highlighting a portionof the fluctuation components of an input signal.

[0008]FIG. 46 shows how input signals are processed on a conventionaldigital filter computer. Three kinds of digital filters performmultiplication individually for the same input signal and the productsare summed and synthesized at the synthesizer to generate the output. Inthis example, different filter shapes are used and three types ofoutputs each extracting the characteristics of the waveforms are summedto detect the waveform.

[0009] The determinator 20 in FIG. 37 compares the synthesized output(digital filter output) from the computer 9 with the threshold value todetermine the size difference and derive a result of determination(Ds−j). The output could be a monitor 26 that shows the result of thedetermination, or an alarm device 28 such as a lamp connected via acontact output 27.

[0010] In the wavelet system shown in FIG. 41, multiple digital filtermultiplication coefficient patterns are matched to a function patterncalled an elemental pattern. Multiple short-period similar patterns aregenerated from the elemental pattern to determine the intensity of thefrequency components. The shape of the elemental pattern determineswhich components are extracted from the incoming signal. These similarpatterns are used to generate filters of a different frequency band tomatch the length of the multiplication coefficient pattern. Themultiplication coefficient pattern also determines the type of signalprocessing used, such as integration and differentiation, so that oncethe elemental pattern is determined multiple digital filters with asimilar multiplication coefficient pattern perform the same signalprocessing where only the frequency band is different. In the waveletsystem, the scale (length along the time axis) of the basicmultiplication coefficient pattern for identifying correlations withinput signals is equal to the length of several wavelengths of therespective frequencies. Thus, compared with Fourier transform, thewavelet system can analyze power spectra of a relatively short timeinterval.

[0011] In FIG. 42, a multiplication coefficient pattern typically usedin a conventional wavelet system is used to derive the digital filteroutputs from an input signal. A wavelet generally has a scale severaltimes longer than the wavelength of the lowest frequency to becharacterized. The reference time axis is positioned at the, center ofthe multiplication coefficient pattern column. The pattern extends onboth sides of the axis in the same phase. Some effective output isderived when the input signal arrives at the center of the delay memorywith a sequential delay, and an inner product is calculated with thecoefficient located at the center of the pattern column. This means thata detection delay proportional to the wavelength always exists whenidentifying input signal data using this type of multiplicationcoefficient pattern.

[0012] The wavelet is suitable for analyzing cyclic signals that lastfor a certain time period. It has been used for analyzing voice andvibrations of a certain length, and also for textures (image quality,elemental patterns, etc.) that extend over a certain distance. The signsembedded in time-series data do not have vibration components in manycases. The difference in texture between the entire time-series data andthe area containing signs is small as the scale of the multiplicationcoefficient pattern becomes larger. It is thus difficult to detect atransient decrease in this case. The size of the scale is a majorproblem in analyzing non-cyclic signals with small repetitive vibrationsor small-area images. It is difficult for the wavelets to characterizeone-time pulses such as those in the input signals shown in FIGS. 31A,31B, 31C and 31D. This is because the wavelet focuses on the dampingcurves and undulation of specified sounds, and as such, it does noteffectively identify signals by sound reverberation. When the rise andfall of an input signal have a different waveform such as shown in FIG.42, identifying only the rising waveform is difficult, with the resultthat the normal waveform WA and abnormal waveform WB are notdifferentiated, and the system reacts strongly to the normal waveformWC.

[0013] Apart from the above, many studies are being undertaken torealize human-friendly control by performing 1/f fluctuation conversionfor input waveforms. FIG. 43 shows a typical conventional 1/ffluctuation waveform generator. Conventional low-pass (LPF) andhigh-pass filters (HPF) are combined to approximately generate the 1/ffluctuation waveforms. Random waveforms are input and the high-passfilter coefficients are adjusted to derive the 1/f fluctuationwaveforms. This means that the characteristic of the conventionallow-pass filter is damped by a tilt of −2 or more. The tilt of thetarget 1/f fluctuation is −1. The high-pass filters with a tilt of 2 ormore, which are multiplied by coefficient k, are combined in parallel tomake a filter set. Adjusting coefficient k derives a tilt ofapproximately −1. Filter sets of a different band are connected inseries to expand the filter characteristics to a wider frequency region.

[0014] The problem with this system is that precision operationalamplifiers must be used to construct the target filters with electricelements, and the production involves very large-expenses. To constructthe original, the filters of the above hardware are used as the abovedigital filters, and then filter sets are constructed. This can beachieved relatively easily. For example, the conventional multiplicationcoefficient pattern-(A) shown in FIG. 29 may be used as themultiplication coefficient pattern of the low-pass filter LPF. High-passfilters HPF can also be configured in like manner. However, low-passfilters (LPFs) and high-pass filters (HPFs) have a different phase gap(difference of time of change between input and output) so that it isdifficult to synthesize multiplication coefficient patterns of bothfilters to generate a new one. A more complex means is required togenerate a multiplication coefficient pattern for high-pass filters(HPFs) by converting the value of coefficient k and then synthesizingthe patterns of both filters.

[0015] The other conventional technique uses ½-time integration.

[0016] The input is a random progression derived from random functions,and the power spectrum of the output is approximated to 1/f fluctuation.The details have been described in a book (Kazuo Tanaka; IntelligentControl System [Intelligent Control by Fuzzy Neuro, 6A Chaos], KyoritsuShuppan Co.). Of the above low-pass filters (LPFs), the digital filterDf with a primary delay with a number of integration equivalent to 1will generate outputs of the power spectrum with a −2 tilt. To perform a−1 tilt power spectrum conversion, the number of integration is reducedto less than 1 to smooth the random progression. FIGS. 44A, 44B, and 44Cshow a multiplication coefficient pattern of ½-time integration with8-tap digital filters Dfs (FIG. 44A), and the resultant conversionoutputs (FIGS. 44D and 44C). FIGS. 45A, 45B and 45C show amultiplication coefficient pattern of ⅓-time integration with 8-tapdigital filters Dfs (FIG. 45A), and the resultant conversion outputs(FIGS. 45B and 45C). The number of integrations is less than 1 in bothcases. To derive a smooth 1/f fluctuation with a −1 tilt, the number oftaps must be increased. The power spectrum curve will not be smooth ifthe number of digital filter taps is small.

[0017] In consideration of the above condition, the present inventionsolves the problems of conventional wavelet systems by:

[0018] a. Contrary to the underlying concept of conventional waveletsystems, it ignores frequency separation characteristics and insteadmaximizes the phase characteristics (ability to identify undulation andother anomalies), and

[0019] b. Makes final determination by matching the timing of phasedelay (gap of detection time) of multiple filters.

[0020] Another objective of the present invention is to solve the aboveproblems of conventional 1/f fluctuation conversion by setting andshaping the digital filter multiplication coefficient patterns usingmathematical formulae and software.

[0021] The present invention focuses attention on the fact that thedigital filters (Dfs) emit outputs by predicting the transition of inputwaveforms when signals are input and a portion of thefluctuation-components of, the input signal-is-highlighted. The presentinvention therefore uses digital filters (Dfs) to output 1/f fluctuationwaveforms and other specific waveforms using non-integer n-timeintegration.

[0022] The present invention discerns sound and noise and other one-timeundulation in time-series data and also identifies the characteristic ofindividual single pulses (pulsation). As a result, it can be used forpredictive diagnosis and determination of acceptance or rejection ofmerchandise. It is also possible for the system of the present inventionto input random waveforms and output specific waveforms with a frequencycomponent distribution such as 1/f fluctuation waveforms. A similartechnique is known at this time, in which undulation and pulse waveformsare approximated with a sequential line and the sequential linecoordinates are input to discern the difference of patterns in a neuralnetwork. This technique involves complex procedures and huge computationoverhead so that the system cost is quite high. Furthermore, slow pulsesare processed but real-time processing is impossible. The presentinvention characterizes a wide range of waveforms from pulse soundscontaining sharp and high-frequency components to very long cyclictime-series data with little change recurring slowly over a long timeperiod. Furthermore, the processing procedure is not complex. Thepresent invention can also easily output specific waveforms such as 1/ffluctuation waveforms by-effectively using digital filters embedded inthe above waveform converter.

DISCLOSURE OF THE INVENTION

[0023] The present invention provides the following means to solve theabove technical problems:

[0024] A waveform detection system 10 comprising a sensor 3, a signalinput 1, a computer 9 to characterize signal data based on signalsoutput from the signal input 1, a determinator 20 to identify thecharacteristics of the waveforms based on the output of the computer 9,and an output 25 to show the result of the determinator 20, wherein thecomputer 9 has a digital filter calculator 13, a phase-matchingparameter setter 19, and a synthesizer 17; the digital filter calculator13 has digital filters DFs each equipped with a delay memory 16 to storeand delay input signals and a multiplication coefficient pattern memory14 to store multiplication coefficient patterns, and a parameter setter15; the parameter setter 15 has a multiplication coefficient patternsetter 30 to set multiplication coefficient pattern and a filterparameter setter 29; the digital filter calculator 13 connects signaldata input from the signal input 1 to the digital filters Dfs,calculating and outputting the sum of products for the contents of bothmemories; the outputs of the digital filter calculator 13 are merged atthe synthesizer 17, and signs of state change are characterized based onthe synthesized output, wherein, furthermore, multiple digital filtersare installed at the digital filter calculator 13 in the computer; thecoefficient patterns derived from non-integer n-time integration areused as elemental multiplication coefficient patterns; a means isprovided to change the phase of at least one of the elemental patterns,input signal data, and digital filter output (phase-matching parametersetter 19); and the outputs of digital filters Dfs that use theelemental patterns are synthesized in a state where the portion of thephases of the characteristics extracting and processing function ischanged.

[0025] The means is also a waveform detection system 10 equipped with acomputer 9 to characterize signal data and a parameter input, whereinthe computer 9 has a digital filter calculator 13 and a parameter input;the digital filter calculator 13 has digital filters Dfs each equippedwith a delay memory 16 to store and delay input signals and amultiplication coefficient pattern memory 14 to store multiplicationcoefficient patterns, and a-parameter setter 15; the parameter setter 15has a multiplication coefficient pattern setter 30 to set multiplicationcoefficient pattern via a parameter input and a filter parameter setter29; the digital filter calculator 13 connects signal data input from theinput to the digital filters Dfs calculating and outputting the sum ofproducts for the contents of both memories; the multiplicationcoefficient patterns are prepared to have a simply decreasing or simplyincreasing tilt; and the output of the digital filter calculator 13 is aconversion output, wherein, furthermore, the multiplication coefficientpatterns are elemental multiplication coefficient patterns derived fromnon-integer n-time integration; and the number of integration n to get afrequency response power spectrum tilt of 1 or −1 in a portion of thefrequency band is used to adjust the digital filter output.

[0026] The means is also a state-monitoring system wherein thecharacteristics of a waveform are extracted from the signal data inputusing the waveform detection system 10 of the above structure, and basedon the above characterized waveform, the state of the input signals isdetermined and displayed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0027]FIG. 1 is a block diagram showing a waveform detection systemaccording to one of the embodiments of the present invention;

[0028]FIG. 2 is a block diagram showing a parameter setter of thewaveform detection system shown in FIG. 1;

[0029]FIG. 3A is a block diagram showing general digital filterfunctions of a computer;

[0030]FIG. 3B is a block diagram-showing a computer incorporating thedigital filters shown in FIG. 3A;

[0031]FIG. 3C is a block diagram showing a phase memory provided on theinlet side of the digital filter;

[0032] FIGS. 4A-4G are graphs showing conventional procedure for settingmultiplication coefficient patterns;

[0033]FIG. 5 shows graphs of generation of elemental patterns fromn-time integration using the present invention;

[0034]FIG. 6 shows a graph of different shapes of elemental patternsderived from n-time integration using the present invention;

[0035]FIG. 7 is an example using the pattern shown in FIG. 4F as themultiplication coefficient pattern;

[0036]FIG. 8 shows an example using the pattern shown in FIG. 4G as themultiplication coefficient pattern;

[0037]FIG. 9 is a graph showing three kinds of waveforms representinginput signal data for testing;

[0038]FIG. 10 is a graph showing two kinds of digital filter outputswhere the input signal data for testing shown in FIG. 9 are used in theintegration characteristic patterns in FIGS. 7 and 8;

[0039]FIG. 11 is a graph showing a comparison of multiplicationcoefficient patterns for testing between a conventional technique andthe method by means of the present invention;

[0040]FIG. 12 is a graph showing digital filter outputs where aconventional multiplication coefficient pattern and a multiplicationcoefficient pattern by means of the present invention are applied to theinput signals for testing shown in FIG. 9;

[0041] FIGS. 13A-13D are graphs showing two elemental patternssynthesized to generate the multiplication coefficient pattern shown inFIG. 11;

[0042] FIGS. 14A-14C are block diagrams showing comparison ofpre-synthesis of elemental patterns and synthesis of outputs.

[0043]FIG. 15 is a graph showing multiplication coefficient patterns(integration P−1);

[0044]FIG. 16 is a graph of process to characterize the shape ofpulse-type input signal waveforms using a synthesized pattern;

[0045]FIG. 17 is a graph showing continuous plot of FIG. 16;

[0046]FIG. 18 is a graph showing a negative integrated characteristicpattern;

[0047]FIG. 19 is a graph showing a differentiated characteristic patternof a special shape;

[0048]FIG. 20 is a graph of four input waveforms for testing;

[0049]FIG. 21 is a graph showing that digital filter outputs are derivedfrom input signals shown in FIG. 20 using the differentiatedcharacteristic pattern shown in FIG. 19;

[0050]FIG. 22 is a graph showing the difference of outputs from thethree characteristic patterns;

[0051]FIG. 23 is a graph showing that-outputs of integration P−2 shownin FIG. 22 are synthesized after inverting the sign to identify inputwaveform 1 shown in FIG. 20;

[0052]FIG. 24 is a graph showing that outputs of differentiation P aredelayed by 8 and the sign of the negative peak of differentiation P isinverted before synthesis to identify input waveform 2 shown in FIG. 20;

[0053]FIG. 25 is a graph showing that outputs of integration P−1 aredelayed by 8 and the sign of the negative peak of differentiation P isinverted before synthesis to identify input waveform 3 shown in FIG. 19;

[0054]FIG. 26 is a block diagram showing a structure of the waveformdetecting function;

[0055]FIG. 27 is a block diagram showing derivation of weighted averagein creating a synthetic output;

[0056]FIG. 28 is a block diagram showing a partial view of thedeterminator;

[0057] FIGS. 29A-29C are graphs showing comparison of digital filteroutputs for the same input signals derived from conventionalmultiplication coefficient patterns and from the multiplicationcoefficient patterns by means of the present invention;

[0058]FIG. 30 is a block diagram showing a waveform detection system ofthe present invention applied to a state-monitoring system;

[0059] FIGS. 31A-31D are graphs showing comparison of detected waveformsderived from the above state-monitoring system;

[0060]FIG. 32 is a graph showing comparison of digital filter outputsderived from multiplication coefficient patterns used in conventionalwavelet methods shown on FIG. 41 and those derived from multiplicationcoefficient patterns of the present invention;

[0061]FIG. 33 is a block diagram showing a digital filter functioncomprising another digital filter calculator;

[0062]FIG. 34 is a block diagram showing a structure of a digital filterfunction comprising another digital filter calculator;

[0063] FIGS. 35A-35C are multiplication coefficient pattern synthesizedusing these elemental patterns (FIG. 35A), and relevant conversionoutput diagrams (FIGS. 35B and 35C);

[0064]FIG. 36 is a development flowchart for multiplication coefficientpattern by means of the present invention;

[0065]FIG. 37 is a block diagram showing a structure of a conventionalwavelet type waveform detection system;

[0066]FIG. 38 is a block diagram showing a structure of a conventionalparameter setter;

[0067]FIG. 39 is an illustration of a general digital filter serving asthe base of wavelet calculation;

[0068]FIG. 40 is a diagram showing a digital filter calculationprocedure;

[0069]FIG. 41 is a combined graph and diagram showing a conventionalwavelet method;

[0070]FIG. 42 is a graph showing a n example of deriving outputs fromdigital filters using multiplication coefficient patterns typicallyemployed in conventional wavelet methods;

[0071]FIG. 43 is a block diagram showing a conventional 1/f fluctuatingwaveform generator;

[0072] FIGS. 44A-44C are graphs showing multiplication coefficientpatterns of ½-time integration with 8-tap digital filters (FIG. 44A),and the relevant conversion output diagrams (FIGS. 44B and 44C);

[0073] FIGS. 45A-44C are graphs showing multiplication coefficientpatterns of ⅓-time integration with the same 8-tap digital filters asused in FIG. 44A, and the relevant conversion output diagrams (FIGS. 45Band 45C); and

[0074]FIG. 46 is a combined block diagram and graphs showing calculationof the sum of products and synthesis.

DETAILED DESCRIPTION OF THE PREFERRED-EMBODIMENTS

[0075] Preferred working examples of the waveform detection system withthe signal processing function of the present invention andstate-monitoring systems using the system are described below. FIG. 1shows the structure of a working example of the waveform detectionsystem. The system comprises, as shown in the figure, a sensor 3, signalinput 1, computer 9, determinator 20, and output 25. These hardwarecomponents are the same as those used in the above wavelet type waveformdetection system. The main structural features of the system aredescribed below again.

[0076] The signal input 1 comprises a converter 23 that collects sensoroutput data, an A/D converter 4, and a memory 5. The signal input 1converts measurement values sent from the sensor 3 into digital data.The memory 5 stores the input signals in files, and sends data asprocessing at the computer progresses to generate input (measurement)signal data 8 at the computer 9. The memory 5 is not essential ifsignals are processed on a real-time basis. In this case, the real-timemeasurement data are digitized and sent directly to the computer 9.

[0077] The computer 9 comprises a signal processor 11, a digital filtercalculator 13 comprising multiple digital filters Df1-Dfn and aparameter setter 15, a synthesizer 17 to integrate the outputs, and aphase-matching parameter setter 19. The signal processor 11 is notessential, but is installed as required. As an independent device, thesignal processor 11 is responsible for-reducing noise, normalizing data,and distributing multiple data.

[0078] The digital filter Df has a delay memory 16 to store and delayinput signals and a multiplication coefficient pattern memory 14 tostore multiplication coefficient-patterns. The parameter setter 15 has afilter parameter setter 29 and a multiplication coefficient patternsetter 30 as shown in FIG. 2. At the computer 9, a phase memory DI isinstalled in each of the digital filters Dfs, and the phase-matchingparameter setter 19 determines the delay time operation of the phasememory DI. Outputs from multiple digital filters Dfs are individuallydelayed to match the phase, and the waveforms are synthesized at thesynthesizer 17 and synthesized output (Dt-j) is output.

[0079] An example of signal processing at the digital filter calculator13 is shown in FIG. 46. Three kinds of digital filters Dfs togetherperform a sum of products calculation for the same input signal. Theoutputs are then synthesized at the synthesizer 17. The filters Dfs havea different shape from each another in this example, and the three kindsof outputs each extracting the features of waveforms are summed todetect the waveform.

[0080] The multiple digital filters Dfs use an elemental pattern for thebase of multiplication coefficient patterns of each digital filter Df.The shape of a multiplication coefficient pattern is determined usingthe phase, number of taps, and the sum of coefficients of the elementalpattern as the parameters. Two alternatives exist: one is to synthesizewaveforms at the synthesizer 17 using outputs from multiple digitalfilters Dfs that use their respective elemental patterns and the otheris to first synthesize elemental patterns to generate a-new singlemultiplication coefficient pattern, which is set to a single digitalfilter Df to generate outputs. Both methods produce an identicalwaveform conversion effect. In a converter which processes alreadysynthesized multiple elemental patterns, phase matching and waveformsynthesizing are already in the new synthesized multiplicationcoefficient pattern, and thus both are an essential component. Thesynthesized output from the computer 9 is compared with the thresholdvalue at the determinator 20 to determine the size and generate theresult of determination (Ds-j). At the output 25, the result ofdetermination is displayed on a monitor 26, or activates an alarm lamp(alarm device) 28 or other warning device via a contact output 27.

[0081] The Method (procedure) for generating a synthesized patterns isdescribed below as shown in the flow chart in FIG. 36.

[0082] This flow chart is used to enter the parameters for elementalpatterns (JK1), calculate the elemental patterns, and generate a pattern(jk0) by synthesizing the elemental patterns that are entered.

[0083] Step S1: Zero-clear JK0 (synthesized pattern).

[0084] Step S2: Repeat loop (1) until designation of elemental patterncompletes.

[0085] Step S3: Specify file input or no file input. Memory designationallowed if elemental patterns exist in memory.

[0086] Step S4: Enter base (number of taps), a parameter for theelemental pattern in FIG. 7.

[0087] Step S5: Enter number of integration n, a parameter for theelemental pattern in FIG. 7.

[0088] Step S6: Enter polarity (+, −), a parameter for the elementalpattern in FIG. 7.

[0089] Step S7: Enter gap for generating a synthesized pattern.

[0090] Step S8: Calculate elemental pattern by linear approximationusing parameters for the elemental pattern specified in Steps S4 throughS7. Set the result in JK1.

[0091] Step S9: Read elemental pattern from file and set in JK1.Alternatively, copy elemental pattern from other memory to JK1.

[0092] Step S10: Determinator if i=1.

[0093] Step S11: Add elemental pattern (JK2) specified or created inSteps S4 through S9 to JK0 (synthesized pattern).

[0094] Step S12: Add 1 to counter I.

[0095] Step 13: If necessary, save JK generated in Steps S through S12in a file.

[0096] This completes the generation of a synthesized pattern.

[0097] The internal structure of the above computer 9 is described belowin more detail.

[0098]FIG. 3A illustrates the digital filter function comprising thedigital filter calculator 13. FIGS. 3B and 3C illustrate a computer 9equipped with the digital filters Dfs shown in FIG. 3A.

[0099] Digital filter Df1 accepts input signal data Ij, multiplicationcoefficient pattern P1, filter parameters, and clock 20 signals. Aftercomputation, digital filter output (O1−j) is generated.

[0100] Input signal data Ij is an array like time-series data. Themultiplication coefficient pattern is a pattern signal derived from aprocedure to be described later. Filter parameters are data inputs setby the above filter parameter setter 29. They comprise the number ofdigital filter taps, multiplication coefficient pattern data, synthesisweighting pattern data specified for the synthesizer 17, and othercomponents. In this particular example, the phase-matching parametersetter 19 specifies phase-matching parameters, which are additional tothe filter parameters.

[0101] The phase-matching parameter specifies a delay time for a phasememory DI that is installed in each digital filter, in addition to thedelay memory 16. One of the simple methods is to use the number of tapsof the phase memory DI as a parameter. When the number of taps is set bythe parameter, a clock counter is installed to temporarily store theoutput of digital filters. When the clock counter is equal to the presetnumber-of taps the temporarily stored output is output as a delayoutput.

[0102] When the timing of an input signal is delayed, the relevantfilter output is correspondingly delayed but the output itself remainsthe same. This is because the characteristic of a digital filter Df isuniquely determined by the multiplication coefficient pattern. For thisreason, no functional difference occurs whether the above phase memoryDI is installed on the output side of the digital filter Df, as shown inFIG. 3B, or on the input side, as shown in FIG. 3C.

[0103] The above hardware and its configuration are exactly the same asthat of a conventional wavelet type waveform detection system.

[0104] The procedure for setting multiplication coefficient patterns fordigital filters Dfs, the feature of the present invention, is describedbelow. The feature of the present invention is that numericalexpressions are used to model multiplication coefficient patterns andthe multiplication coefficient patterns are set using software.

[0105] As an example, non-cyclic and non-repetitive transient variationsare assumed and the procedure for setting multiplication coefficientpatterns for characterizing the variations is described below.

[0106] The multiplication coefficient pattern may be used bytransferring those progressions that are already input into theparameter setter in the computer 9 shown in FIG. 1 to the multiplicationcoefficient pattern memory 14 of respective digital filters Dfs.Changing the pattern transforms the characteristics of digital filtersDfs and thus the pattern is normally renewed when the system isoperating or used in a semi-stationary (stand-by) state.

[0107] FIGS. 4A-4G show the procedure for setting the multiplicationcoefficient pattern. The horizontal axis represents time t. The time ismore recent as you move rightward on the graph. The pattern in FIG. 4Ais typical of a conventional digital filter used as a band-pass filter.When the number of taps of a digital filter is set to n in actualapplication, FIG. 4A is split into n sections in the direction of timeand the values corresponding to the split points are taken as aprogression to make a multiplication coefficient pattern. Period tc of acycle shown in FIG. 4A determines the frequency characteristic of thefilter. When the pattern shown in FIG. 4A is used, the filterselectively extracts the waveforms of frequency f=tc/2É

.

[0108] The present invention uses the multiplication coefficient patterndevelopment procedure disclosed in the publication of unexamined patentapplication No. 260066-1998. Using this procedure, the waveforms on theleft side of the center in FIG. 4A are selected to keep the pastwaveforms and the waveforms, on the right side of the center are deletedas shown in FIG. 4B. All these are performed on a computer screen. Thewaveform equivalent to ¼tc from the center is picked as shown in FIG. 4Cand the remainder is deleted to derive a waveform section shown in FIG.4D. The waveform shown in FIG. 4D represents a one-quarter period of thefundamental waveform. Periodic vibration components are not saved, butthe shape of the rising fundamental waveform is kept. The right-sideedge of the waveform (or the center of the figure) represents the mostrecent time. This point is used in the calculation with the most recentdata when measurement data is given as input signals. The shape of therise in FIG. 4D is used as the multiplication coefficient pattern fordigital filters Dfs. When the incoming signals have the same shape asthe rise of the fundamental waveform, the output (or the result ofmatching) is the highest. When the incoming signals have a differentrising shape, the output decreases. The output values vary with thelevel of matching-of the rising shape, so that a digital filter Dfassigned with a specific pattern can detect the phases differences fromthe fundamental waveform and output the result. The pattern in FIG. 4Dmay be used as it is. In the publication of unexamined patentapplication No. 260066-1998, a straight-line pattern with the base ta(=¼ tc) is defined as an elemental pattern, which is further abstractedinto simple elemental patterns. Phase difference is detected usingmultiple phase combinations.

[0109] The above procedure to select sectional figures on a computerscreen to define multiplication coefficient patterns like those shown inFIGS. 4F and 4G necessitate calculation of the linear sum of multipleelemental patterns at the synthesizer in order to derive a waveform fromthe outputs of digital filters. This involves the following problems:

[0110] a. The required number of elemental pattern parameters to definea multiplication coefficient pattern is 2× (number of elementalpatterns). The operator must use at least 4 parameters to develop afilter pattern. This can lead to a great number of trials and errors.

[0111] b. Even if the operator defines the tap length to determine themajor frequency characteristics for the digital filters, the shape willvary when combining multiple parameters. It is not easy for the operatorto estimate and determine the filter shape or digital filtercharacteristics;

[0112] To solve these problems, the present invention proposes the useof the formula described below to define multiplication coefficientpatterns suitable for various waveforms. The procedure for using thisformula is described below as shown in FIG. 5.

[0113] The elemental multiplication coefficient patterns are calculatedand defined using non-integer n-time integration in the presentinvention.

[0114] Non integer n-time integration is represented by the followingformula (1):${{{formula}\quad (1)}:\quad {I\quad n\quad \left( {\varnothing (t)} \right)}} = {\left( {1/{\Gamma (n)}} \right){\int{{\varnothing \left( {t - \tau} \right)}{\tau\hat{}\left( {n - 1} \right)}{\tau}\quad \left( {{{range}\quad {of}\quad {integration}\quad 0} \leq \tau < \infty} \right)}}}$

[0115] where:

[0116] Ø (t): input waveform

[0117] Γ (n): known nth-order r function of the following expression:$\begin{matrix}{{\Gamma (n)} = {\int{{e\hat{}{- \left( {t - \tau} \right)}}{\tau\hat{}\left( {n - 1} \right)}{\tau}\quad \left( {{{range}\quad {of}\quad {integration}\quad 0} \leq \tau < \infty} \right)}}} & (2)\end{matrix}$

[0118] The coefficient τ{circumflex over ( )}(n−1) for Ø(t) on the rightside of formula (1) represents the outline of the multiplicationcoefficient pattern:

y0=τ{circumflex over ( )}(n−1   (3)

[0119] to calculate inner product for input waveforms with X as the timevariable. The outline is a simple decreasing curve that is a downwardconvex shape for the number of integration n<1 as shown in FIG. 5. Thenumber of integration n=0.5 for the case described in FIG. 5.

[0120] In case of n=1, the outline is like a step function, or 1 in theintegration section and 0 in other sections.

[0121] Conventional 1/f fluctuation conversion with digital filtersinvolves the problem of an increased number of taps to get smooth 1/ffluctuation characteristics with −1 tilt for the FFT spectrum usingnon-integer-time integration.

[0122] It may be desirable to decrease the time delay for digital filteroutputs to lessen the computation overhead. This can indeed be achievedby decreasing the number of taps to use but if this is done the endpoint will not approach zero smoothly but will suddenly drop to zero.This causes a bias damping peculiar to digital filters Dfs. The powerspectrum curve is never smooth if this occurs. To solve this problem, wemultiply the linear equation for processing the end point shown in FIG.5:

y1=1−(1/L)(τ−1)  (4)

[0123] where:

[0124] L: number of filter taps

[0125] by y0 to get:

y=y0×y1  (5)

[0126] Function y presents an outline that is a downward convex shapelike equation (2), with y1 approaching zero smoothly. We deriveelemental patterns by substituting the values for the points along thetime axis of function y (equation 5) in the multiplication coefficientpatterns.

[0127] For n<0, no mathematical meaning exists but the shape of adigital filter Df approaches zero more sharply, which is meaningful inthe present invention.

[0128]FIG. 6 shows the variable shapes of elemental patterns due to thenumber of integration n. The figure covers the range of 0 through −1.The value n uniquely determines the shape of the filter coefficientpattern. It is also easy to adjust the shape and frequencycharacteristics relative to the target waveform simply by changing thevalue of n and the number of filter taps. This leads to significantlydecreased trials and errors during adjustment by the operator. No limitapplies to the value n so far as the shape of the filter coefficientpattern is concerned provided that a means is used to make the divergingside approach zero asymptotically in equation (3).

[0129] Each elemental pattern consists of positive (+) polarity only.When inputting a digital filter signal that changes from zero to one ata certain time and remains at that level thereafter (step-like waveformsignal), the sum of the products with the elemental pattern is, afterthe input signal continues as I for a certain time, equal to-the sum ofall coefficients each multiplied by 1 for outputs of a finite value, andthis value is maintained.

[0130] This means that the above elemental pattern performs a kind ofpartial integration over the length (=interval) of the digital filter(integration characteristic pattern). In like manner, those patternswith an opposite tilt or-patterns with negative polarity also produce anelemental pattern respectively, acquiring the integration characteristicas in the above case. To assign a differentiation characteristic for thesame input to get zero output, the elemental patterns are arrangedsymmetrically as shown in FIG. 4G so that the sum of all coefficientsequals zero (differentiation characteristic pattern). In this case, thenegative (−) side load pattern shows the falling characteristic. If thelength is the same ta in this particular case, the filter will detectphase difference with the fundamental frequency in which rise and fallare uniquely determined by ta.

[0131]FIGS. 7 and 8 show the order of the patterns when the patterns inFIGS. 4F and 4G are set as multiplication coefficient patterns. The timeis more recent towards the left side of the graph. FIGS. 7 and 8 showthe multiplication coefficient patterns with the integration anddifferentiation characteristic, respectively.

[0132]FIG. 9 shows waveforms of the three input signal data usedfor-testing. Both rise and fall are-gentler with the increasing width.All of these multiplication coefficient patterns are set with softwareusing the above formulas.

[0133]FIG. 10 shows the output waveforms from two kinds of digitalfilters. The input signal data for testing shown in FIG. 9 are used asinputs to derive integration characteristic patterns shown in FIGS. 7and 8. The output of the filter with the integration characteristicpattern rises with a time delay to the input waveform, reaches a-certainlevel where it stays, and then falls gradually also with a time delay tothe input waveform. The time delay of the waveform is equivalent to atemporary storage of a portion of the input signals in the digitalfilter, or integration by parts. This function is the same as thefunction of a low-pass filter used in electronic circuits, and can beused to remove noise and smooth input signals.

[0134] The output of the filter with the differentiation characteristicpattern responds to the rise of input signals with a sharp pulse. Asharp pulse is also output at the rise of an input. The output is zerowhen the input is constant and remains. The output is large when thetilt of the rise or fall of an input is large. This type of filter witha differentiation characteristic is suitable for detecting tilts. Toclarify the difference between a conventional multiplication coefficientpattern such as shown in FIG. 4A and the pattern of the presentinvention, we use multiplication coefficient patterns for testing shownin FIG. 11. As is clear in the figure, the conventional pattern requires29 taps although the pattern is simplified from that shown in FIG. 4A.With the present invention, in contrast, the system operates with only 7taps, or as short as less than one-fourth of the conventional system.

[0135] A conventional multiplication coefficient pattern and themultiplication coefficient pattern of the present invention are appliedto the input signals for testing shown in FIG. 9. The resultant digitalfilter outputs are shown in FIG. 12. Time is calculated for each clock.The input signal moves one tap for each calculation in the delay memory.With the conventional pattern, the output waveform repeats vibrationwhile the pattern by means of the present invention generates onepulse-like waveform for each change in inputs.

[0136] As described above, the present invention significantly decreasesthe number of taps of a digital filter to be used, reduces theinformation processing workload, improves delay in detection of changein inputs, and facilitates determination because the outputs are singlepulses.

[0137] The multiplication coefficient pattern of the present inventionshown in FIG. 11 is synthesized from two elemental patterns in theprocedure described below. The two elemental patterns shown in FIGS. 13Aand 13B are synthesized with a time delay equal to time (=number oftaps) ta to derive a differentiation characteristic pattern shown inFIG. 13C. In like manner, a pattern shown in FIG. 13D is derived with atime delay smaller than time ta. Delaying the time (=number of taps) bya certain value is equivalent to outputting data after temporarilystoring in phase memory for a certain time period.

[0138] Pre-synthesis of elemental patterns and synthesis of outputs aredescribed below as shown in FIG. 14.

[0139]FIG. 14A describes pre-synthesis of elemental patterns for settinga single multiplication coefficient pattern. FIGS. 14B and 14Cillustrate the synthesizing of outputs. In this case, a phase memory DIis provided on either the input or output of digital filters Dfs thathave elemental patterns. The results are identical for all three casesof FIGS. 14A, 14B, and 14C. The multiplication coefficient pattern shownin FIG. 15 is a synthesis of the two patterns of tilt 0.5 and length 8and 17, respectively. The two patterns are synthesized using theprocedure described in FIG. 13.

[0140]FIG. 16 describes the process for characterizing pulse-like inputsignal waveforms using the above synthesized patterns. Waveforms t1 andt2 are not actual waveforms but are plotted by projecting themultiplication coefficient patterns on two drawings overtime. Thedigital filters Dfs capture input signals sequentially. This isequivalent to the multiplication coefficient patterns moving to theright sequentially in FIG. 16. The position of the right-hand edge ofthe pattern represents the most recent input to the digital filter. Att1, input signals are zero, and no way to perform multiplication. At t2multiplication is made at four points. The sum is 220 at the output.

[0141]FIG. 17 shows continuous plotting of the outputs shown in FIG. 16.Given this particular relationship between the multiplicationcoefficient pattern and input signals, the sum of products with themultiplication coefficient patterns peaks a little after the inputpeaks. This is because the input rise interval is short. In like manner,we generate a negative integration characteristic pattern (FIG. 18) anda differentiation characteristic pattern of a special shape (FIG. 19).Using these three synthesized patterns, we identify the four waveformsshown in FIG. 20 using the procedure described below. The characteristicpatterns each with a unique characteristic respond differently to theshape of input waveforms. The combination used for identification mustbe carefully prepared after understanding which pattern becomes large ona sharp rise of an input or by using other characteristics.

[0142] Using the differentiation characteristic pattern shown in FIG.19, we characterized the input signals shown in FIG. 20. The result isgiven in FIG. 21. As is clear from FIG. 21, the output is sharp andstrong for a sharp rise or fall with a large tilt. The difference ofoutputs was tested on the three characteristic patterns as shown in FIG.22. The outputs from these three characteristic patterns are namedintegration P−1 (pattern), integration P−2 and differentiation p,respectively. Possible combinations can use time lag and inversion ofsigns for waveforms. The latter is equivalent to shifting the-phase by180 degrees, and thus both are phase operations.

[0143] In FIG. 22, to identify input waveform WF1, we need to delay theapplication of differentiation P that characterizes the rise of inputwaveforms by 10 (a time equivalent to 10 taps), invert the sign ofintegration P−2 that generates a high output at a gentle fall andsynthesize both patterns. As a result, we derive an output that is thelargest for input waveform WF1 as shown in FIG. 23. To identify inputwaveform WF2, we delay differentiation P by 8 (an 8-tap equivalenttime), invert the negative sign of peak of differentiation P, andsynthesize both patterns. The result is shown in FIG. 24. In likemanner, to identify input waveform WF3, we delay integration P−1 by 8(an 8-tap equivalent time), invert the negative sign of peak ofdifferentiation P, and synthesize both patterns. The result is shown inFIG. 25.

[0144] The function for identifying input-signal waveforms WF1 throughWF4 is shown in FIG. 26. The digital-filter output (Normal: N) indicatesa normal output. Output (Invert: I) is an inverted output. The functionfor detecting waveform WF4 in FIG. 26 is not required if there is noother waveform to be detected, besides waveforms WF1 through WF4. Allthe above descriptions assume the synthesis of two digital filteroutputs-at the synthesizer or 1:1 synthesis of delay outputs.

[0145] If it is found that some patterns are effective at discerningfeatures while the others make little contribution, a proportionalweight is given to them before synthesizing the patterns. This isdescribed below as shown in FIG. 27. It is also possible to reduce allcharacteristic patterns to elemental patterns, and synthesize them withphase matching (delay of output, inversion, etc.) of multiple filtersincorporating elemental multiplication coefficient patterns. Synthesizedweight patterns are used to calculate the weighted average of digitalfilter outputs or their delay outputs Odi−j after delay processing.Delay outputs Odi−j are multiplied by weight wi, and all results aresummed and then divided by the sum of weights w1+w2+w3+w4 . . . wi=Σ(wn)(n 5=1 to i).

[0146] The use of weight varies with programs. Weighting may be used insumming where a weight is simply multiplied to derive the sum. It mayalso be used in processing geometrical average.

[0147] A determinator function may also be provided to determine thedigital filter outputs O1·j through Oj·j individually before determiningfused and synthesized output at the determinator 20 as shown in FIG. 28.When the waveform of input signal is a series of significant signalssuch as phoneme and radio wave signals, the phoneme and signals may becharacterized; the phoneme is determined after checking the size withthreshold values; the results are temporarily stored in a phase memoryDI; the data are timed with the result of determination of next phonemeand signals; and the data are synthesized for final determination. Insuch an application as well, a phase memory DI may be positioned at theinput of the digital filter calculator 13 to derive the same effects.

[0148] The waveform detection system of the present invention isconstructed as stated above. It is compared with a conventional waveletwaveform detection system below. As shown in FIG. 29A, themultiplication coefficient pattern A is a conventional waveletmultiplication coefficient pattern while the pattern B shown in FIG. 29Bis a multiplication coefficient pattern by means of the presentinvention. Patterns A and B are applied to the same input signals andthe outputs are obtained. The result is shown in graphs G1 and G2 inFIG. 29C. The first graph shows the input signals, that is, detectedradio waves. The sharp peaked pulse near the center is noise. Smallpeaked waveforms on the right and left hand side are the waveforms to beextracted. The performance of a detector is determined by its ability toignore the central noise and highlight the target waveforms.

[0149] Graphs G1 and G2 show the result of the detection using aconventional wavelet method and using the present invention,respectively. Both succeed in detecting the target waveforms despite thenoise, but detection using the conventional system is considerablydelayed. When using digital filter outputs derived from themultiplication coefficient pattern B that is assimilated only to thetilt of waveforms to be detected, instead of pattern A, the delay indetection is reduced to approximately one-half that when using patternA. This means that a multiplication coefficient pattern of a simple formincreases throughput. With pattern B, it is possible to change thenumber of taps and integration parameter n in such a direction thatoutput errors decrease on changing the tilt of the pattern. This meansthat a learning system can easily be developed to perform automaticadjustments. It is obvious that changing and adjusting the shape isquite difficult with pattern A.

[0150] An example of an application of the above waveform detectionsystem 10 to a state-monitoring system is described below as shown inFIG. 30. The state-monitoring system has the structure shown in thefigure. Detection signals from the sensor 3 are input into the waveformdetection system 10 via amplifiers 37 and-A/D converter 4, and arecomputed and processed. The system performs, for example, predictivediagnosis for pressure waveforms at pressure transfer pipes that maybecome clogged with radioactive substances. Changes in pressurewaveforms are captured at the waveform detection system (detector) tomonitor the state of clogging in FIG. 31D, the input signals arepressure waveforms. Peak P1 is a normal waveform, and peak P2 an anomalyprecursor waveform. The conventional multiplication coefficient patternA shown in FIG. 31A, a graph G1 in FIG. 31D, produces a similar strengthof digital filter outputs for peaks P1 and P2, making it impossible todiscriminate between them by threshold determination.

[0151] Graphs G2 and G3 in FIG. 31D result from the use of the rise andfall characteristic detection multiplication coefficient patterns,respectively. The rise characteristic is identical for both normal andanomaly precursor waveforms so the result is the same. The fallcharacteristic clearly reveals a difference. When graph G2 is delayed bytime td and added to graph G3, the result of the detection of normalwaveforms takes all negative values while the result for anomalyprecursor waveforms takes predominantly positive values. We cantherefore detect the precursor signals. Delaying waveforms and matchingpeaks is called phase matching, which is another feature of the presentinvention. The result of detection is displayed by means of a knownmeans, making it clear whether the pressure transfer pipes are cloggedor not.

[0152] An example of characterization focusing on the undulation ofwaveforms is shown in FIG. 32. The figure shows the output of digitalfilters DFs that use the multiplication coefficient patterns of thepresent invention for the purpose of comparison with the outputs ofdigital filters Dfs that use the multiplication coefficient patternstypically adopted in conventional wavelet methods. The multiplicationcoefficient pattern used is a differentiation characteristic pattern.The digital filter Df has 7 taps. As is clear in FIG. 37, and incomparison with FIG. 42, the output for Normal OA, Abnormal OB, andNormal OC sections of undulation of input signals in FIG. 32 is a singlepulse-like output. The output for Normal OC is negative and the outputfor Abnormal OB is the largest of all outputs. Anomalies are detected ifthe threshold value is set between 130 and 200 at the determinator 22.As is also clear from the figure, the delay of detection is almostinsignificant compared with 10 to 20 tap equivalent delay in FIG. 42.Early detection is possible using the present invention. The structureof the waveform converter to create a known 1/f fluctuation filter isdescribed below. The same known waveform converter hardware is used asis.

[0153]FIG. 33 illustrates the-basic system of performing 1/f fluctuationconversion using digital filters Dfs of a waveform detection system 10.FIG. 33 shows a method for detecting-waveforms using the computer 9incorporated in the waveform detection system shown in FIG. 1. Thedigital filter Df shown in FIG. 33 is not essential to the synthesizer17 shown in FIG. 1. The multiplication coefficient patterns for digitalfilters Dfs are set by the parameter setter 15. It is also-possible toset multiplication coefficient patterns that are transferred externallyvia parameter input 38.

[0154]FIG. 33 shows the digital filter function comprising a digitalfilter calculator 13. The digital filter function used is the same asthat described above in FIG. 3A. FIG. 34 is a conceptual drawing forFIG. 33. Pa in FIG. 34 represents digital filters DFs and is calculatedusing the following equation:

Pa=P 1+P 2+P 3+ . . . +Pi  (6)

[0155] Pi in FIG. 34 is the multiplication coefficient pattern that isset for the individual digital filters Dfs. The multiplicationcoefficient pattern is calculated using the above equation.

[0156] The output waveforms derived from synthesizing the outputs of theindividual digital filters Dfs shown in FIG. 34 are identical with theoutput waveforms of the digital filter Df with multiplicationcoefficient pattern Pa shown in FIG. 34. It is possible to developmultiplication coefficient patterns for 1/f fluctuation filters usingthe above waveform converter by means of procedures other than thosedisclosed in the present, invention. To be specific two or more digitalfilters Dfs, each of which has an elemental pattern, are combined andtheir outputs synthesized, or alternatively, elemental patterns arecombined to generate a new multiplication coefficient pattern. In thelatter case, since any multiplication coefficient pattern settingparameters must be set to generate a filter, and it is time-consumingfor the operator to generate a filter pattern.

[0157] The elemental pattern development procedure using the presentinvention requires setting of only two parameters; the number of digitalfilter taps L and the number of integration n using the above formulas.The merits are:

[0158] a. The parameters to be controlled in setting a multiplicationcoefficient pattern are few so that trials and errors are reduced.

[0159] b. The shape of a digital filter is uniquely defined by thenumber of integration n, so that it is easy for the operator to estimateand determine the shape or filter characteristics.

[0160]FIG. 35A shows a synthesized multiplication coefficient patternfrom elemental patterns. FIG. 35B is a power spectrum diagram derivedfrom a digital filter featuring the synthesized pattern. Comparing thispower spectrum diagram with the conventional one shown in FIG. 44; weknow that the tilt of power spectrum-shown in FIG. 35B is smoother thanthe conventional one or −1, which indicates that the tilt is closest tothe tilt of 1/f fluctuation.

[0161] As described above, with the waveform detection system of thepresent invention, the shape of the multiplication coefficient patternof digital filters is designed to maximize the phase characteristicability to perceive undulation and other anomalies) by sacrificingfrequency separation characteristics that are the basic concept ofconventional wavelet systems. With the system of the present invention,the phase delay (gap of detection time) of multiple filters isdetermined at the same time. For these reasons, the waveform detectionsystem of the present invention characterizes a wide range of waveformsfrom pulse sounds containing sharp and high-frequency components to verylong cyclic time-series data with little change recurring slowly over along time period. The system also easily outputs specific waveforms suchas 1/f fluctuation waveform. The above working example uses computers inthe same way that conventional wavelet-type operations do. It ispossible to configure the system by combining pieces of hardware withrespective functions. The above-mentioned embodiment is only an example,and the present invention may be implemented in various other forms ofembodiment without deviating from the spirit and essential featuresthereof.

INDUSTRIAL APPLICATION

[0162] As described above in detail, the present invention providesimproved high-speed response over conventional wavelet systems and theunit is more compact because the waveform detection system is embeddedin sensor amplifiers or monitors. It is easy to detect low frequencies,undulation, and sharp pulses, and it also easily generatesmultiplication coefficient patterns. For these reasons, the system ofthe present invention can be easily adapted to various systems.

[0163] It also exhibits excellent effects such as ease of outputtingspecific waveforms such as 1/f fluctuation waveforms.

What is claimed is:
 1. A waveform detection system comprising: a sensor,a signal input, a computer to characterize signal data based on signalsoutput from the signal input, a determinator to determine thecharacteristics of waveforms based on the output of the computer, and anoutput to show the result of determination of the determinator, whereinthe computer has a digital filter calculator, a phase matching parametersetter, and a synthesizer; the digital filter calculator has digitalfilters each equipped with a delay memory to store and delay inputsignals and a multiplication coefficient pattern memory to storemultiplication coefficient patterns, and a parameter setter; theparameter setter has a multiplication coefficient pattern setter to setmultiplication coefficient pattern and a filter parameter setter; thedigital filter calculator connects signal data input from the signalinput to the digital filters, calculating and outputting the sum ofproducts for the contents of both memories; the outputs of the digitalfilter calculator are merged at the synthesizer, and signs of statechange are characterized based on the synthesized output, wherein,furthermore, multiple digital filters are installed at the digitalfilter calculator in the computer; the coefficient patterns derived fromnon-integer n-time integration are used as elemental multiplicationcoefficient patterns; a means is provided to change the phase of atleast one of the elemental patterns, input signal data, and digitalfilter output (phase-matching parameter setter); and the outputs ofdigital filters that use the elemental patterns are synthesized in astate where a portion of the phases of the characteristic extracting andprocessing function is changed.
 2. A waveform detection system asclaimed in claim 1 wherein a phase memory to delay input or output ofthe digital filter for a certain time period is installed; the phasematching parameter setter is used to set a delay time such that thephase of the output matches the phase of other digital filters; and theoutputs from digital filters are synthesized and transferred: to thedeterminator.
 3. A waveform detection system as claimed in claim 2wherein the maximum or minimum output of the digital filter istemporarily stored in the phase memory, and the outputs of multipledigital filters are synthesized for determination.
 4. A waveformdetection system as claimed in claim 3 wherein a determinator functionto determine output values is provided on the output of digital filters;the values are temporarily stored in the phase memory; and the phases ofthe result of determination are matched before deriving a synthesizedoutput.
 5. A waveform detection system as claimed in claim 1 wherein inplace of matching phases such as by delaying input or output of digitalfilters using the phase changing means (phase-matching parameter setter)of the computer the parameters are transferred from the phase-matchingparameter setter to the-multiplication coefficient pattern setter of theparameter setter, to change the phase of multiplication coefficientpatterns, and a new multiplication coefficient pattern is generated bysynthesizing the multiple multiplication coefficient patterns andsetting them to the digital filters.
 6. A waveform detection systemcomprising: a computer that characterizes signal data and a parameterinput, wherein the computer has a digital filter calculator and aparameter input; the digital filter calculator has digital filters eachequipped with a delay memory to store and delay input signals and amultiplication coefficient pattern memory to store multiplicationcoefficient patterns, and a parameter setter; the parameter setter has amultiplication coefficient pattern setter to set multiplicationcoefficient pattern via a parameter input and a filter parameter setter;the digital filter calculator connects signal data input from the inputto the digital filters, calculating and outputting the sum of productsfor the contents of both memories; the multiplication coefficientpatterns are so made as to have a simply decreasing or simply increasingtilt; and the output of the digital filter calculator is a conversionoutput, wherein, furthermore, the multiplication coefficient patternsare elemental multiplication coefficient patterns derived fromnon-integer n-time integration; and the digital filter output isadjustable using the number of integration n such that the powerspectrum tilt-of the frequency response is 1 or −1 in a portion of thefrequency band.
 7. A waveform detection system as claimed in claim 6wherein elemental pattern 1 is selected from the multiplicationcoefficient pattern; a similar elemental pattern 2 of the opposite signto pattern 1 is selected from the multiplication coefficient pattern;elemental patterns 1 and 2 are positioned with their leads staggered;the values of coefficients on the same position are added to generate anew multiplication coefficient pattern; and the new pattern is appliedto the above digital filter so that the tilt of the power spectrumrelative to the input signal (frequency response of output signal) is 2(f2-power fluctuation conversion).
 8. A waveform detection system asclaimed in claim 7 wherein a coefficient pattern derived from the abovenon-integer n-time integration and the multiplication coefficientpattern making up the above f2-power fluctuation conversion are added togenerate a new multiplication coefficient pattern; and the new patternis applied to the above digital filter so that the tilt of powerspectrum of output signals relative to the input signal is 1 (ffluctuation conversion) and the value of the output predicts the valueof an upcoming input.
 9. A waveform detection system as claimed in claim7 or 8 wherein correction is made so that the zero-approaching end ofthe multiplication coefficient pattern will approach zero gradually inorder to prevent-bias damping intrinsic to digital informationprocessing.
 10. A state-monitoring system incorporating a waveformdetection system claimed in any of claims 1 through 9 wherein thecharacters of waveforms are extracted from signal data transferred fromthe input; the characterized waveforms are used to determine the stateof input signals; and the result of the determination is displayed.